Data Analysis Journal 

Author

MyFitnessPal

MyFitnessPal provides powerful tools that make it easier for everyone to live a healthier life by tracking meals and physical activity.

Sr. Manager, Business Intelligence

vidIQ

vidIQ is the first YouTube audience development and management suite that helps brands and agencies grow their views and subscribers.

Senior Product Analyst

Primitive Logic, a Logic20/20 Company

Logic20/20 helps transform your business faster by focusing on simple and elegant solutions to business challenges.

Senior Consultant

Microsoft

First Republic Bank

Senior Data Analyst

Change.org

Change.org, the world's largest platform for social change, with over 180 million users. We empower people everywhere to create the change they want to see and enable companies to engage in dialogue with online movements.

Quantitative Analyst

User Advocacy Data Specialist

Analytics and Data Science leader with a passion for developing great products and tackling data challenges. I connect business with technology, bring data insights into decision-making, and mentor analysts.

One of the reasons measuring retention is hard: there are at least 20 ways to do it. And all methods can be right.

Here’s the framework I use - 3 layers of retention analytics, each with its own purpose, logic, and data structure. Done right, this setup becomes the foundation of trusted retention analytics and everything else really.

1️⃣ Consolidated KPI
Your North Star. A single, stable metric (it could be logo retention or revenue retention) that reflects overall business health. 
It should be built in SQL, not tool defaults or vendor definitions.

2️⃣ Segmented Trends
Retention over time by week/month, by user type, by platform. 
This is the hardest to get right because the retention method matters (N-Day vs. Unbounded), user segments need to be finalized (core vs. power), and you have to account for monetization models and product type.

3️⃣ Cohorted Retention
The actionable layer. Cohorts let you see how different signup groups behave, diagnose onboarding/activation, and measure product changes. 
This works best if you maintain a log of business changes, product launches, and marketing initiatives that you can map back to cohorts.

Once you lock down these 3 layers, retention analytics becomes one of the most powerful levers for understanding product health. You can build other reports on top of it. 📈 

The hardest part in building this out isn’t the SQL or the math. 
It’s choosing the right time windows, the right retention type, and aligning the definition across your organization. And if your company maintains multiple products, you’ll need to unify different retention models into one consolidated high-level approach. Easier said than done.

Here’s my full approach (with SQL examples and data setups), breaking down retention types, how to interpret reports, and how to choose the right retention logic for your product: https://lnkd.in/gYHPkk-5

Taplio

Olga Berezovsky's Linkedin Analytics

's Best Posts (last 30 days)

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Olga Berezovsky's Linkedin Analytics

's Best Posts .css-qqfgvy{font-size:var(--chakra-fontSizes-md);}(last 30 days)

Want to drive more opportunities from LinkedIn?

Olga Berezovsky's Linkedin Analytics

's Best Posts (last 30 days)